
@MISC{_survey,
    author = {Elisabeth A. Wong, Brittany Baur},
    title = {On Network Tools for Network Motif Finding: A Survey Study},
    year = {}
}

@MISC{_analybionetwork,
    author = {Roded Sharan, Adi Shabi, Tomer Benyami},
    title = {Analysis of Biological Networks: Network Motifs PPI Networks: Data Processing and functional Annotation},
    year = {}
}


@MISC{_analybionetwork,
    author = {Roded Sharan, Adi Shabi, Tomer Benyami},
    title = {Analysis of Biological Networks: Network Motifs PPI Networks: Data Processing and functional Annotation},
    year = {}
}

@article{Alon01072008,
author = {Alon, Noga and Dao, Phuong and Hajirasouliha, Iman and Hormozdiari, Fereydoun and Sahinalp, S. Cenk},
title = {Biomolecular network motif counting and discovery by color coding},
volume = {24},
number = {13},
pages = {i241-i249},
year = {2008},
doi = {10.1093/bioinformatics/btn163},
abstract ={Proteinâ~@~Sprotein interaction (PPI) networks of many organisms share global topological features such as degree distribution, k-hop reachability, betweenness and closeness. Yet, some of these networks can differ significantly from the others in terms of local structures: e.g. the number of specific network motifs can vary significantly among PPI networks.Counting the number of network motifs provides a major challenge to compare biomolecular networks. Recently developed algorithms have been able to count the number of induced occurrences of subgraphs with kâ~I¤ 7 vertices. Yet no practical algorithm exists for counting non-induced occurrences, or counting subgraphs with kâ~I¥ 8 vertices. Counting non-induced occurrences of network motifs is not only challenging but also quite desirable as available PPI networks include several false interactions and miss many others.In this article, we show how to apply the â~@~Xcolor codingâ~@~Y technique for counting non-induced occurrences of subgraph topologies in the form of trees and bounded treewidth subgraphs. Our algorithm can count all occurrences of motif Gâ~@² with k vertices in a network G with n vertices in time polynomial with n, provided k=O(log n). We use our algorithm to obtain â~@~Xtreeletâ~@~Y distributions for kâ~I¤ 10 of available PPI networks of unicellular organisms (Saccharomyces cerevisiae Escherichia coli and Helicobacter Pyloris), which are all quite similar, and a multicellular organism (Caenorhabditis elegans) which is significantly different. Furthermore, the treelet distribution of the unicellular organisms are similar to that obtained by the â~@~Xduplication modelâ~@~Y but are quite different from that of the â~@~Xpreferential attachment modelâ~@~Y. The treelet distribution is robust w.r.t. sparsification with bait/edge coverage of 70% but differences can be observed when bait/edge coverage drops to 50%.Contact:cenk@cs.sfu.ca},
URL = {http://bioinformatics.oxfordjournals.org/content/24/13/i241.abstract},
eprint = {http://bioinformatics.oxfordjournals.org/content/24/13/i241.full.pdf+html},
journal = {Bioinformatics}
}

@Article{PhysRevE.71.016110,
  title = {Systematic identification of statistically significant network measures},
  author = {Ziv, Etay  and Koytcheff, Robin  and Middendorf, Manuel  and Wiggins, Chris },
  journal = {Phys. Rev. E},
  volume = {71},
  number = {1},
  pages = {016110},
  numpages = {8},
  year = {2005},
  month = {Jan},
  doi = {10.1103/PhysRevE.71.016110},
  publisher = {American Physical Society}
}

@MISC{_ogata,
    author = {Hiroyuki Ogata, Wataru Fujibuchi, Susumu Goto, and Minoru Kanehisa},
    title = {A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters},
    year = {}
}

@article{Ciriello,
author = {Ciriello, Giovanni and Guerra, Concettina},
title = {A review on models and algorithms for motif discovery in protein-protein interaction networks},
volume = {7},
number = {2},
pages = {147-156},
year = {2008}
}

@MISC{_lassig,
    author = {Berg J, Lässig M.},
    title = {Local graph alignment and motif search in biological networks},
    year = {}
}

@article{Milo25102002,
author = {Milo, R. and Shen-Orr, S. and Itzkovitz, S. and Kashtan, N. and Chklovskii, D. and Alon, U.},
title = {Network Motifs: Simple Building Blocks of Complex Networks},
volume = {298},
number = {5594},
pages = {824-827},
year = {2002},
doi = {10.1126/science.298.5594.824},
abstract ={Complex networks are studied across many fields of science. To uncover their structural design principles, we defined â~@~\network motifs,â~@~] patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.},
URL = {http://www.sciencemag.org/content/298/5594/824.abstract},
eprint = {http://www.sciencemag.org/content/298/5594/824.full.pdf},
journal = {Science}
}

@Article{kavosh,
AUTHOR = {Kashani, Zahra and Ahrabian, Hayedeh and Elahi, Elahe and Nowzari-Dalini, Abbas and Ansari, Elnaz and Asadi, Sahar and Mohammadi, Shahin and Schreiber, Falk and Masoudi-Nejad, Ali},
TITLE = {Kavosh: a new algorithm for finding network motifs},
JOURNAL = {BMC Bioinformatics},
VOLUME = {10},
YEAR = {2009},
NUMBER = {1},
PAGES = {318},
URL = {http://www.biomedcentral.com/1471-2105/10/318},
DOI = {10.1186/1471-2105-10-318},
PubMedID = {19799800},
ISSN = {1471-2105},
ABSTRACT = {BACKGROUND:Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.RESULTS:We present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of E. coli and S. cereviciae, and also on non-biological networks: a social and an electronic network.CONCLUSION:The efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: http://Lbb.ut.ac.ir/Download/LBBsoft/Kavosh/.},
}



